Exploring Text Recognition Segmentation and Detection in Natural Scene Images
Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2104/ http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf http://eprints.intimal.edu.my/2104/2/642 |
| _version_ | 1848766921157115904 |
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| author | Wydyanto, . Maria, Ulfa |
| author_facet | Wydyanto, . Maria, Ulfa |
| author_sort | Wydyanto, . |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Identification, segmentation, and recognition of fonts from real-world images are major
challenges in computer vision, particularly due to subtle differences in font shapes, lighting,
and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms
for text detection, segmentation, and recognition from natural scene images. A variety of
techniques are assessed for their use in natural settings, including deep learning-based methods,
region proposal, and feature-based detection. There is additional discussion of the difficulties
of managing changes in text properties such as font type, size, orientation, and noise and
occlusion disruptions. This survey also looks at preprocessing techniques like filtering and
illumination normalization that are meant to increase the accuracy of text detection. In light of
the findings of the literature analysis, this study concludes that the combination of adaptive
segmentation techniques with deep learning-based recognition models offers promising
performance in text recognition in natural scenery images. This survey provides a foundation
for the development of more effective and robust methods for future applications in the fields
of image processing and computer vision. |
| first_indexed | 2025-11-14T11:58:49Z |
| format | Article |
| id | intimal-2104 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:49Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21042024-12-26T06:38:23Z http://eprints.intimal.edu.my/2104/ Exploring Text Recognition Segmentation and Detection in Natural Scene Images Wydyanto, . Maria, Ulfa Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, segmentation, and recognition from natural scene images. A variety of techniques are assessed for their use in natural settings, including deep learning-based methods, region proposal, and feature-based detection. There is additional discussion of the difficulties of managing changes in text properties such as font type, size, orientation, and noise and occlusion disruptions. This survey also looks at preprocessing techniques like filtering and illumination normalization that are meant to increase the accuracy of text detection. In light of the findings of the literature analysis, this study concludes that the combination of adaptive segmentation techniques with deep learning-based recognition models offers promising performance in text recognition in natural scenery images. This survey provides a foundation for the development of more effective and robust methods for future applications in the fields of image processing and computer vision. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf text en cc_by_4 http://eprints.intimal.edu.my/2104/2/642 Wydyanto, . and Maria, Ulfa (2024) Exploring Text Recognition Segmentation and Detection in Natural Scene Images. Journal of Data Science, 2024 (66). pp. 1-18. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Wydyanto, . Maria, Ulfa Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title | Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title_full | Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title_fullStr | Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title_full_unstemmed | Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title_short | Exploring Text Recognition Segmentation and Detection in Natural Scene Images |
| title_sort | exploring text recognition segmentation and detection in natural scene images |
| topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/2104/ http://eprints.intimal.edu.my/2104/ http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf http://eprints.intimal.edu.my/2104/2/642 |